Attribute Reduction Method Based on Generalized Grey Relational Analysis and Decision-Making Trial and Evaluation Laboratory

Zhiwen Zhang,Limin Gong,Yujun Xue,Xiaoying Yang,Junxing Li

doi:10.1109/access.2020.3014237

Abstract

Attribute reduction is a challenging issue in intelligent manufacturing. Existing methods are mainly based on rough set theory (RST) focusing on symbolic and discrete values. However, classical RST doesn't consider the complex interrelationship among conditional attributes and consecutive attribute values. Our article seeks to deal with this problem by proposing a hybrid framework based on generalized grey relational analysis (GGRA) and decision-making trial and evaluation laboratory (DEMATEL) method. GGRA is used to determine the initial importance of conditional attributes relative to the decision attribute and the direct relationship among conditional attributes. The causal relationships among conditional attributes are calculated by the DEMATEL method. Then we calculate the final importance of the conditional attribute relative to the decision attribute and apply a threshold to control the number of attributes entering the core set. Finally, an illustrative case and experiments are verified our method of attribute reduction with consecutive attribute values and complex interrelationship among attributes.

Highlights

Intelligent manufacturing is a key measure to gain competitive advantages for the manufacturing industry of major countries around the world [1]
To achieve the attribute reduction in intelligent manufacturing, we propose a new attribute reduction method based on generalized grey relational analysis (GGRA) and decision making trial and evaluation laboratory (DEMATEL)
Compared with the traditional Grey relational analysis (GRA) and GGRA, the time complexity of the proposed method is larger because the algorithm calculates the interaction among conditional attributes, which increases the accuracy of attribute reduction

Summary

INTRODUCTION

Intelligent manufacturing is a key measure to gain competitive advantages for the manufacturing industry of major countries around the world [1]. To achieve the attribute reduction in intelligent manufacturing, we propose a new attribute reduction method based on generalized grey relational analysis (GGRA) and decision making trial and evaluation laboratory (DEMATEL). Grey information is another challenge for the attribute reduction algorithm [7]. It includes the grey absolute correlation degree, the grey relative correlation degree, and the grey comprehensive correlation degree [48], which we’ll show in the following part These help in analyzing the relevance of research objects from different perspectives so that the results can reflect the relationship between sequences more accurately. According to the definition of grey comprehensive correlation, the relationship among conditional attributes R(ai, aj) can be obtained, where i, j= 1, 2, . . . ,n

DEMATEL

ATTRIBUTE IMPORTANCE AND ATTRIBUTE REDUCTION

ILLUSTRATIVE CASE AND EXPERIMENTS

ILLUSTRATIVE CASE

EXPERIMENTS

CONCLUSION